{"id":"https://openalex.org/W4401386003","doi":"https://doi.org/10.1145/3583740.3628442","title":"Unveiling Energy Efficiency in Deep Learning: Measurement, Prediction, and Scoring across Edge Devices","display_name":"Unveiling Energy Efficiency in Deep Learning: Measurement, Prediction, and Scoring across Edge Devices","publication_year":2023,"publication_date":"2023-12-06","ids":{"openalex":"https://openalex.org/W4401386003","doi":"https://doi.org/10.1145/3583740.3628442"},"language":"en","primary_location":{"id":"doi:10.1145/3583740.3628442","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3583740.3628442","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Eighth ACM/IEEE Symposium on Edge Computing","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3583740.3628442","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5051059790","display_name":"Xiaolong Tu","orcid":"https://orcid.org/0009-0001-9396-5383"},"institutions":[{"id":"https://openalex.org/I181565077","display_name":"Georgia State University","ror":"https://ror.org/03qt6ba18","country_code":"US","type":"education","lineage":["https://openalex.org/I181565077"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiaolong Tu","raw_affiliation_strings":["Georgia State University, Atlanta, Georgia, USA","Georgia State University"],"raw_orcid":"https://orcid.org/0009-0001-9396-5383","affiliations":[{"raw_affiliation_string":"Georgia State University, Atlanta, Georgia, USA","institution_ids":["https://openalex.org/I181565077"]},{"raw_affiliation_string":"Georgia State University","institution_ids":["https://openalex.org/I181565077"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057681399","display_name":"Anik Mallik","orcid":"https://orcid.org/0000-0002-0566-1460"},"institutions":[{"id":"https://openalex.org/I102149020","display_name":"University of North Carolina at Charlotte","ror":"https://ror.org/04dawnj30","country_code":"US","type":"education","lineage":["https://openalex.org/I102149020"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Anik Mallik","raw_affiliation_strings":["The University of North Carolina at Charlotte, Charlotte, North Carolina, USA","The University of North Carolina at Charlotte"],"raw_orcid":"https://orcid.org/0000-0002-0566-1460","affiliations":[{"raw_affiliation_string":"The University of North Carolina at Charlotte, Charlotte, North Carolina, USA","institution_ids":["https://openalex.org/I102149020"]},{"raw_affiliation_string":"The University of North Carolina at Charlotte","institution_ids":["https://openalex.org/I102149020"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100322085","display_name":"Dawei Chen","orcid":"https://orcid.org/0000-0002-4162-1423"},"institutions":[{"id":"https://openalex.org/I1293612202","display_name":"Toyota Motor Corporation (Switzerland)","ror":"https://ror.org/05p0pbv75","country_code":"CH","type":"company","lineage":["https://openalex.org/I1293612202","https://openalex.org/I4210125472","https://openalex.org/I4210137853"]},{"id":"https://openalex.org/I4210093665","display_name":"Toyota Motor North America (United States)","ror":"https://ror.org/0076knn86","country_code":"US","type":"company","lineage":["https://openalex.org/I4210093665","https://openalex.org/I4210125472","https://openalex.org/I4210137853"]}],"countries":["CH","US"],"is_corresponding":false,"raw_author_name":"Dawei Chen","raw_affiliation_strings":["Toyota Motor North America, Mountain View, California, USA","Toyota InfoTech Labs"],"raw_orcid":"https://orcid.org/0000-0002-4162-1423","affiliations":[{"raw_affiliation_string":"Toyota Motor North America, Mountain View, California, USA","institution_ids":["https://openalex.org/I4210093665"]},{"raw_affiliation_string":"Toyota InfoTech Labs","institution_ids":["https://openalex.org/I1293612202"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009775690","display_name":"Kyungtae Han","orcid":"https://orcid.org/0000-0001-8291-5025"},"institutions":[{"id":"https://openalex.org/I1293612202","display_name":"Toyota Motor Corporation (Switzerland)","ror":"https://ror.org/05p0pbv75","country_code":"CH","type":"company","lineage":["https://openalex.org/I1293612202","https://openalex.org/I4210125472","https://openalex.org/I4210137853"]},{"id":"https://openalex.org/I4210093665","display_name":"Toyota Motor North America (United States)","ror":"https://ror.org/0076knn86","country_code":"US","type":"company","lineage":["https://openalex.org/I4210093665","https://openalex.org/I4210125472","https://openalex.org/I4210137853"]}],"countries":["CH","US"],"is_corresponding":false,"raw_author_name":"Kyungtae Han","raw_affiliation_strings":["Toyota Motor North America, Mountain View, California, USA","Toyota InfoTech Labs"],"raw_orcid":"https://orcid.org/0000-0001-8291-5025","affiliations":[{"raw_affiliation_string":"Toyota Motor North America, Mountain View, California, USA","institution_ids":["https://openalex.org/I4210093665"]},{"raw_affiliation_string":"Toyota InfoTech Labs","institution_ids":["https://openalex.org/I1293612202"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053978714","display_name":"Onur Altintas","orcid":"https://orcid.org/0000-0001-9865-7358"},"institutions":[{"id":"https://openalex.org/I1293612202","display_name":"Toyota Motor Corporation (Switzerland)","ror":"https://ror.org/05p0pbv75","country_code":"CH","type":"company","lineage":["https://openalex.org/I1293612202","https://openalex.org/I4210125472","https://openalex.org/I4210137853"]},{"id":"https://openalex.org/I4210093665","display_name":"Toyota Motor North America (United States)","ror":"https://ror.org/0076knn86","country_code":"US","type":"company","lineage":["https://openalex.org/I4210093665","https://openalex.org/I4210125472","https://openalex.org/I4210137853"]}],"countries":["CH","US"],"is_corresponding":false,"raw_author_name":"Onur Altintas","raw_affiliation_strings":["Toyota Motors North America, Mountain View, California, USA","Toyota InfoTech Labs"],"raw_orcid":"https://orcid.org/0000-0001-9865-7358","affiliations":[{"raw_affiliation_string":"Toyota Motors North America, Mountain View, California, USA","institution_ids":["https://openalex.org/I4210093665"]},{"raw_affiliation_string":"Toyota InfoTech Labs","institution_ids":["https://openalex.org/I1293612202"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5077861883","display_name":"Haoxin Wang","orcid":"https://orcid.org/0000-0002-8732-6200"},"institutions":[{"id":"https://openalex.org/I181565077","display_name":"Georgia State University","ror":"https://ror.org/03qt6ba18","country_code":"US","type":"education","lineage":["https://openalex.org/I181565077"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Haoxin Wang","raw_affiliation_strings":["Georgia State University, Atlanta, Georgia, USA"],"raw_orcid":"https://orcid.org/0000-0002-8732-6200","affiliations":[{"raw_affiliation_string":"Georgia State University, Atlanta, Georgia, USA","institution_ids":["https://openalex.org/I181565077"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5019147177","display_name":"Jiang Xie","orcid":"https://orcid.org/0000-0003-0683-4308"},"institutions":[{"id":"https://openalex.org/I102149020","display_name":"University of North Carolina at Charlotte","ror":"https://ror.org/04dawnj30","country_code":"US","type":"education","lineage":["https://openalex.org/I102149020"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jiang Xie","raw_affiliation_strings":["The University of North Carolina at Charlotte, Charlotte, North Carolina, USA"],"raw_orcid":"https://orcid.org/0000-0003-0683-4308","affiliations":[{"raw_affiliation_string":"The University of North Carolina at Charlotte, Charlotte, North Carolina, USA","institution_ids":["https://openalex.org/I102149020"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":26,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"80","last_page":"93"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12238","display_name":"Green IT and Sustainability","score":0.9995999932289124,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12238","display_name":"Green IT and Sustainability","score":0.9995999932289124,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11992","display_name":"CCD and CMOS Imaging Sensors","score":0.9836999773979187,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11165","display_name":"Image and Video Quality Assessment","score":0.9797999858856201,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6250052452087402},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.5818593502044678},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5163106322288513},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5118469595909119},{"id":"https://openalex.org/keywords/efficient-energy-use","display_name":"Efficient energy use","score":0.46773916482925415},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.41128289699554443},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1118495762348175},{"id":"https://openalex.org/keywords/electrical-engineering","display_name":"Electrical engineering","score":0.07859280705451965}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6250052452087402},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.5818593502044678},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5163106322288513},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5118469595909119},{"id":"https://openalex.org/C2742236","wikidata":"https://www.wikidata.org/wiki/Q924713","display_name":"Efficient energy use","level":2,"score":0.46773916482925415},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.41128289699554443},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1118495762348175},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.07859280705451965}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3583740.3628442","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3583740.3628442","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Eighth ACM/IEEE Symposium on Edge Computing","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3583740.3628442","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3583740.3628442","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Eighth ACM/IEEE Symposium on Edge Computing","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.7400000095367432,"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":43,"referenced_works":["https://openalex.org/W1483870316","https://openalex.org/W1501987291","https://openalex.org/W1885185971","https://openalex.org/W1934410531","https://openalex.org/W1993158854","https://openalex.org/W2015941169","https://openalex.org/W2049434052","https://openalex.org/W2096733369","https://openalex.org/W2114979172","https://openalex.org/W2158592639","https://openalex.org/W2177010970","https://openalex.org/W2183341477","https://openalex.org/W2251939518","https://openalex.org/W2270470215","https://openalex.org/W2523714292","https://openalex.org/W2557728737","https://openalex.org/W2607427562","https://openalex.org/W2618530766","https://openalex.org/W2739757502","https://openalex.org/W2787091153","https://openalex.org/W2789282607","https://openalex.org/W2792106437","https://openalex.org/W2792220137","https://openalex.org/W2804032941","https://openalex.org/W2805828232","https://openalex.org/W2891158090","https://openalex.org/W2895432151","https://openalex.org/W2902251695","https://openalex.org/W2905692112","https://openalex.org/W2962824709","https://openalex.org/W2983038091","https://openalex.org/W2998506323","https://openalex.org/W3009985443","https://openalex.org/W3047122373","https://openalex.org/W3107995663","https://openalex.org/W3143455850","https://openalex.org/W3145668479","https://openalex.org/W3165698711","https://openalex.org/W4281707531","https://openalex.org/W4322153971","https://openalex.org/W6713134421","https://openalex.org/W6774015895","https://openalex.org/W6850346328"],"related_works":["https://openalex.org/W2731899572","https://openalex.org/W2961085424","https://openalex.org/W3215138031","https://openalex.org/W4306674287","https://openalex.org/W3009238340","https://openalex.org/W4321369474","https://openalex.org/W4360585206","https://openalex.org/W4285208911","https://openalex.org/W3046775127","https://openalex.org/W3082895349"],"abstract_inverted_index":{"Today,":[0],"deep":[1,72,94],"learning":[2,73],"optimization":[3],"is":[4,23],"primarily":[5],"driven":[6],"by":[7],"research":[8,206],"focused":[9],"on":[10,137,157],"achieving":[11],"high":[12],"inference":[13],"accuracy":[14],"and":[15,37,57,68,119,126,153,168,175,204,222],"reducing":[16],"latency.":[17],"However,":[18],"the":[19,35,38,88,100,128,145,199,205],"energy":[20,43,54,69,89,105,131,140,155,176],"efficiency":[21,58],"aspect":[22],"often":[24],"overlooked,":[25],"possibly":[26],"due":[27],"to":[28,63,150,171],"a":[29,41,50,81,111,213],"lack":[30],"of":[31,40,92,102,114,147,179,201],"sustainability":[32,209],"mindset":[33,200],"in":[34,66,99,210],"field":[36],"absence":[39],"holistic":[42],"dataset.":[44,141],"In":[45],"this":[46],"paper,":[47],"we":[48,79,124,162],"conduct":[49],"threefold":[51],"study,":[52],"including":[53],"measurement,":[55],"prediction,":[56],"scoring,":[59],"with":[60],"an":[61,180,184],"objective":[62],"foster":[64],"transparency":[65],"power":[67,174],"consumption":[70,90,177],"within":[71],"across":[74],"various":[75],"edge":[76,108,134,181,189,211],"devices.":[77],"Firstly,":[78],"present":[80],"detailed,":[82],"first-of-its-kind":[83],"measurement":[84],"study":[85,97],"that":[86,215],"uncovers":[87],"characteristics":[91],"on-device":[93],"learning.":[95],"This":[96],"results":[98,143],"creation":[101],"three":[103],"extensive":[104],"datasets":[106],"for":[107,133,188],"devices,":[109],"covering":[110],"wide":[112],"range":[113],"kernels,":[115],"state-of-the-art":[116],"DNN":[117,159],"models,":[118],"popular":[120],"AI":[121],"applications.":[122],"Secondly,":[123],"design":[125],"implement":[127],"first":[129],"kernel-level":[130,139],"predictors":[132,149],"devices":[135],"based":[136],"our":[138,148,194,217],"Evaluation":[142],"demonstrate":[144],"ability":[146],"provide":[151],"consistent":[152],"accurate":[154],"estimations":[156],"unseen":[158],"models.":[160],"Lastly,":[161],"introduce":[163],"two":[164],"scoring":[165],"metrics,":[166],"PCS":[167],"IECS,":[169],"developed":[170],"convert":[172],"complex":[173],"data":[178],"device":[182,190],"into":[183],"easily":[185],"understandable":[186],"manner":[187],"end-users.":[191],"We":[192],"hope":[193],"work":[195],"can":[196],"help":[197],"shift":[198],"both":[202],"end-users":[203],"community":[207],"towards":[208],"computing,":[212],"principle":[214],"drives":[216],"research.":[218],"Find":[219],"data,":[220],"code,":[221],"more":[223],"up-to-date":[224],"information":[225],"at":[226],"https://amai-gsu.github.io/DeepEn2023.":[227]},"counts_by_year":[{"year":2026,"cited_by_count":6},{"year":2025,"cited_by_count":15},{"year":2024,"cited_by_count":5}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
